Displaying 1 to 15 from 15 results

semantic-segmentation-editor - Web labeling tool for bitmap images and point clouds

  •    Javascript

A web based labeling tool for creating AI training data sets (2D and 3D). The tool has been developed in the context of autonomous driving research. It supports images (.jpg or .png) and point clouds (.pcd). It is a Meteor app developed with React, Paper.js and three.js. (Optional) You can modify settings.json to customize classes data.

PixelAnnotationTool - Annotate quickly images.

  •    C++

Software that allows you to manually and quickly annotate images in directories. The method is pseudo manual because it uses the algorithm watershed marked of OpenCV. The general idea is to manually provide the marker with brushes and then to launch the algorithm. If at first pass the segmentation needs to be corrected, the user can refine the markers by drawing new ones on the erroneous areas (as shown on video below). Donating is very simple - and secure. Please click here to make a donation.

cvat - Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

  •    Javascript

CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.




OpenLabeling - Open Source labeling tool to generate the training data in the format YOLO requires.

  •    Python

Bounding box labeler tool to generate the training data in the format YOLO v2 requires. The idea is to use OpenCV so that later it uses SIFT and Tracking algorithms to make labeling easier.

compose - A machine learning tool for automated prediction engineering

  •    Python

By automating the early stage of the machine learning pipeline, our end user can easily define a task and solve it. See the documentation for more information.

prodigy-recipes - 🍳 Recipes for the Prodigy, our fully scriptable annotation tool

  •    Python

This repository contains a collection of recipes for Prodigy, our scriptable annotation tool for text, images and other data. In order to use this repo, you'll need a license for Prodigy – see this page for more details. For questions and bug reports, please use the Prodigy Support Forum. If you've found a mistake or bug, feel free to submit a pull request. ✨ Important note: The recipes in this repository aren't 100% identical to the built-in recipes shipped with Prodigy. They've been edited to include comments and more information, and some of them have been simplified to make it easier to follow what's going on, and to use them as the basis for a custom recipe.

finetuner - Finetuning any DNN for better embedding on neural search tasks

  •    Python

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks. It accompanies Jina to deliver the last mile of performance for domain-specific neural search applications. 🎛 Designed for finetuning: a human-in-the-loop deep learning tool for leveling up your pretrained models in domain-specific neural search applications.


TagAnomaly - Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

  •    

Anomaly detection labeling tool, specifically for multiple time series (one time series per category). This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

data-annotator-for-machine-learning - Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning

  •    TypeScript

Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitating the creation and management of annotations. DAML project team welcomes contributions from the community. For more detailed information, see CONTRIBUTING.md.

BMW-Labeltool-Lite - This repository provides you with an easy-to-use labeling tool for State-of-the-art Deep Learning training purposes

  •    CSharp

Additionally, it is possible to connect a pre-trained or a custom-trained model to the LabelTool lite. This functionality allows one to accelerate the labeling process whereby the connected model can be actively used to suggest appropriate labels for each image. We provide a sample dataset in case you don't have your own custom dataset.

jupyterlab-prodigy - 🧬 A JupyterLab extension for annotating data with Prodigy

  •    TypeScript

This repo contains a JupyterLab extension for Prodigy, our scriptable annotation tool for creating training data for machine learning models. It lets you run Prodigy within a JupyterLab tab, and annotate as you develop your models and applications. In order to use this extension, you'll need a license for Prodigy – see this page for more details. For questions, please use the Prodigy Support Forum. If you've found a bug, feel free to submit a pull request. To use this extension, you need JupyterLab >= 2.0.0 ⚠️ and Prodigy.






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